Z-Scores. Location of Scores and Standardized Distributions

Chapter 5: z-Scores: Location of Scores and Standardized Distributions Introduction to z-Scores • • • • In the previous two chapters, we introduced the concepts of the mean and the standard deviation as methods for describing an entire distribution of scores. Now we will shift attention to the individual scores within a distribution. In this chapter, we introduce a statistical technique that uses the mean and the standard deviation to transform each score (X value) into a z-score or a standard score. The purpose of z-scores, or standard scores, is to identify and describe the exact location of every score in a distribution. Introduction to z-Scores cont. • In other words, the process of transforming X values into z-scores serves two useful purposes: – Each z-score tells the exact location of the original X value within the distribution. – The z-scores form a standardized distribution that can be directly compared to other distributions that also have been transformed into z-scores. 1 Z-Scores and Location in a Distribution • • • One of the primary purposes of a z-score is to describe the exact location of a score within a distribution. The z-score accomplishes this goal by transforming each X value into a signed number (+ or -) so that: – The sign tells whether the score is located above (+) or below (-) the mean, and – The number tells the distance between the score and the mean in terms of the number of standard deviations. Thus, in a distribution of IQ scores with μ= 100 and σ = 15, a score of X = 130 would be transformed into z = +2.00. Z-Scores and Location in a Distribution cont. • • • The z value indicates that the score is located above the mean (+) by a distance of 2 standard deviations (30 points). Definition: A z-score specifies the precise location of each X value within a distribution. – The sign of the z-score (+ or -) signifies whether the score is above the mean (positive) or below the mean (negative). – The numerical value of the z-score specifies the distance from the mean by counting the number of standard deviations between X and μ. Notice that a z-score always consists of two parts: a sign (+ or -) and a magnitude. Z-Scores and Location in a Distribution cont. • • • • • Both parts are necessary to describe completely where a raw score is located within a distribution. Figure 5.3 shows a population distribution with various positions identified by their z-score values. Notice that all z-scores above the mean are positive and all z-scores below the mean are negative. The sign of a z-score tells you immediately whether the score is located above or below the mean. Also, note that a z-score of z =+1.00 corresponds to a position exactly 1 standard deviation above the mean. 2 Z-Scores and Location in a Distribution cont. • • • • A z-score of z = +2.00 is always located exactly 2 standard deviations above the mean. The numerical value of the z-score tells you the number of standard deviations from the mean. Finally, you should notice that Figure 5.3 does not give any specific values for the population mean or the standard deviation. The locations identified by z-scores are the same for all distributions, no matter what mean or standard deviation the distributions may have. Fig. 5-3, p. 141 z-Score Formula • The formula for transforming scores into z-scores is Formula 5.1 • • The numerator of the equation, X - μ, is a deviation score (Chapter 4, page 110); it measures the distance in points between X and μ and indicates whether X is located above or below the mean. The deviation score is then divided by σ because we want the z-score to measure distance in terms of standard deviation units. 3 Determining a Raw Score (X) from a z-Score • • Although the z-score equation (Formula 5.1…slide #9) works well for transforming X values into z-scores, it can be awkward when you are trying to work in the opposite direction and change z-scores b k iinto back t X values. l The formula to convert a z-score into a raw score (X) is as follows: Formula 5.2 Determining a Raw Score (X) from a z-Score cont. • • In the formula (from the previous slide), the value of zσ is the deviation of X and determines both the direction and the size of the distance from the mean. Finally, you should realize that Formula 5.1 and Formula 5.2 are actually two diff different t versions i off th the same equation. ti Other Relationships Between z, X, μ, and, σ • • • • In most cases, we simply transform scores (X values) into z-scores, or change z-scores back into X values. However, you should realize that a z-score establishes a relationship between the score, the mean, and the standard d i ti deviation. This relationship can be used to answer a variety of different questions about scores and the distributions in which they are located. Please review the following example (next slide). 4 Other Relationships Between z, X, μ, and, σ cont. • • In a population with a mean of μ = 65, a score of X = 59 corresponds to z = -2.00. What is the standard deviation for the population? – To answer the question, we begin with the z-score value. – A z-score of -2.00 indicates that the corresponding score is located below the mean by a distance of 2 standard deviations. – By simple subtraction, you can also determine that the score (X = 59) is located below the mean (μ = 65) by a distance of 6 points. Other Relationships Between z, X, μ, and, σ cont. • Thus, 2 standard deviations correspond to a distance of 6 points, which means that 1 standard deviation must be σ = 3. 5 Using z-Scores to Standardize a Distribution • • • • It is possible to transform every X value in a distribution into a corresponding z-score. The result of this process is that the entire distribution of X values is transformed into a distribution of z-scores (Figure 5.5). The new distribution of z-scores has characteristics that make the z-score transformation a very useful tool. Specifically, if every X value is transformed into a z-score, then the distribution of z-scores will have the following properties: Using z-Scores to Standardize a Distribution cont. • Shape – The shape of the z-score distribution will be the same as the original distribution of raw scores. – If the original distribution is negatively skewed, for example, then the z-score distribution will also be negatively skewed. – In other words, transforming raw scores into z-scores does not change anyone's position in the distribution. – Transforming a distribution from X values to z values does not move scores from one position to another; the procedure simply relabels each score (see Figure 5.5). Using z-Scores to Standardize a Distribution cont. • The Mean – The z-score distribution will always have a mean of zero. – In Figure 5.5, the original distribution of X values has a mean of μ = 100. – When this value, X=100, is transformed into a z-score, the result is: – Thus, the original population mean is transformed into a value of zero in the z-score distribution. – The fact that the z-score distribution has a mean of zero makes it easy to identify locations 6 Fig. 5-5, p. 146 Using z-Scores to Standardize a Distribution cont. • The Standard Deviation – The distribution of z-scores will always have a standard deviation of 1. – Figure 5.6 demonstrates this concept with a single distribution that has two sets of labels: the X values along one line and the corresponding z-scores along another line. – Notice that the mean for the distribution of z-scores is zero and the standard deviation is 1. – When any distribution (with any mean or standard deviation) is transformed into z-scores, the resulting distribution will always have a mean of μ = 0 and a standard deviation of σ = 1. Using z-Scores to Standardize a Distribution cont. • • Because all z-score distributions have the same mean and the same standard deviation, the z-score distribution is called a standardized distribution. Definition: A standardized distribution is composed of scores that have been t transformed f d to t create t predetermined d t i d values for μ, and, σ. • Standardized distributions are used to make dissimilar distributions comparable. 7 Using z-Scores for Making Comparisons • • One advantage of standardizing distributions is that it makes it possible to compare different scores or different individuals even though they come from completely different distributions. Normally, if two scores come from diff different t distributions, di t ib ti it iis impossible i ibl to t make any direct comparison between them. – Suppose, for example, Bob received a score of X=60 on a psychology exam and a score of X=56 on a biology test. – For which course should Bob expect the better grade? Using z-Scores for Making Comparisons cont. – Because the scores come from two different distributions, you cannot make any direct comparison. – Without additional information, it is even impossible to determine whether Bob is above or below the mean in either ith distribution. di t ib ti – Before you can begin to make comparisons, you must know the values for the mean and standard deviation for each distribution. – Instead of drawing the two distributions to determine where Bob's two scores are located, we simply can compute the two z-scores to find the two locations. 8 Using z-Scores for Making Comparisons cont. – For psychology, Bob's z-score is: – For biology, Bob's z-score is: – Note that Bob's z-score for biology is +2.0, which means that his test score is 2 standard deviations above the class mean. – On the other hand, his z-score is +1.0 for psychology, or 1 standard deviation above the mean. Using z-Scores for Making Comparisons cont. – In terms of relative class standing, Bob is doing much better in the biology class. – Notice that we cannot compare Bob's two exam scores (X = 60 and X = 56) because the scores come from different di t ib ti distributions with ith diff differentt means and d standard deviations. – However, we can compare the two z-scores because all distributions of z-scores have the same mean (μ = 0) and the same standard deviation (σ = 1). Computing z-Scores for Samples • • • Although z-scores are most commonly used in the context of a population, the same principles can be used to identify individual locations within a sample. The definition of a z-score is the same for a sample as for a population, provided th t you use the that th sample l mean and d the th sample standard deviation to specify each z-score location. Thus, for a sample, each X value is transformed into a z-score so that – The sign of the z-score indicates whether the X value is above (+) or below (-) the sample mean, and 9 Computing z-Scores for Samples cont. – The numerical value of the z-score identifies the distance between the score and the sample mean in terms of the sample standard deviation. – Expressed as a formula, each X value in a sample can be transformed into a z-score as follows: f ll • Similarly, each z-score can be transformed back into an X value, as follows: Standardizing a Sample Distribution • • • If all the scores in a sample are transformed into z-scores, the result is a sample of z-scores. The transformation will have the same properties that exist when a population of X value is transformed into z-scores. Recall this is true for data collected from a population, as well (Refer to slide #16 for the same on population distributions). – Specifically, • The sample of z-scores will have the same shape as the original sample of scores. • The sample of z-scores will have a mean of M=0. Standardizing a Sample Distribution cont. • The sample of z-scores will have a standard deviation of s = 1. – Note that the set of z-scores is still considered to be a sample (Just like the set of X values) and the sample formulas must be used to compute variance i and d standard t d d deviation. d i ti 10